A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring.

OBJECTIVE: Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification. MATERIALS AND METHODS: The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients. RESULTS: Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands. DISCUSSION: According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition. CONCLUSION: Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.

An Evaluation of Spin in the Abstracts of Systematic Reviews and Meta-analyses on the Treatment of Psoriasis: A Cross-sectional Analysis.

Spin is a way of reporting that distorts the true findings; we sought to investigate the prevalence of spin in systematic review abstracts on psoriasis treatments and whether study characteristics were associated with spin. We searched MEDLINE and Embase to obtain our sample. Screening and data extraction were performed in a masked duplicate fashion. Each included study was evaluated for the 9 most severe types of spin and other study characteristics. The methodological quality was assessed to explore potential relationships between spin and study quality. Search queries returned 3200 articles, which included 173 systematic reviews. Spin was present in systematic review abstracts. Preventing spin is essential for improving future systematic reviews.

BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS.

With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health data analysts and researchers. However, the EHR data are notoriously noisy due to missing and inaccurate inputs despite the information is abundant. One serious problem is that only a small portion of patients in the database has confirmatory diagnoses while many other patients remain undiagnosed because they did not comply with the recommended examinations. The phenomenon leads to a so-called positive-unlabelled situation and the labels are extremely imbalanced. In this paper, we propose a model-based approach to classify the unlabelled patients by using a Bayesian finite mixture model. We also discuss the label switching issue for the imbalanced data and propose a consensus Monte Carlo approach to address the imbalance issue and improve computational efficiency simultaneously. Simulation studies show that our proposed model-based approach outperforms existing positive-unlabelled learning algorithms. The proposed method is applied on the Cerner EHR for detecting diabetic retinopathy (DR) patients using laboratory measurements. With only 3% confirmatory diagnoses in the EHR database, we estimate the actual DR prevalence to be 25% which coincides with reported findings in the medical literature.

The presence of spin in systematic reviews focused on diabetic neuropathy: A cross-sectional analysis.

BACKGROUND: Spin-the misrepresentation of a study's actual results-has the potential to alter a clinician's interpretation of the study's findings and therefore could affect patient care. Studies have shown spin frequently occurs in abstracts of systematic reviews from a variety of other medical disorders and specialties. AIMS: Our primary aim was to evaluate whether the nine most severe types of spin occurred in systematic review abstracts' concerning diabetic neuropathy treatments. Secondly, we aimed to determine whether spin presence was associated with the methodological quality of a systematic review. METHODS: A search of MEDLINE and Embase collected 1297 articles focused on diabetic neuropathy treatments, of which we included 114 systematic reviews for spin assessment. Each included study was evaluated for the nine most severe types of spin as defined by Yachitz et al. The methodological quality of a systematic review was determined by using the AMSTAR-2 instrument. All screening and data extraction were conducted in a masked, duplicate fashion. Since the final sample size of 114 was not sufficiently powered to do multivariable logistic regression, we calculated unadjusted odds ratios which evaluated relationships between spin presence within abstracts and study characteristics. RESULTS: From the 114 articles reviewed, spin was present in 7.9% of the studies (9/114), with spin type 5: "conclusion claims the beneficial effect of the experimental treatment despite the high risk of bias in the included primary studies" as the most frequent in our study. Spin types 1, 2, 6, and 8 were not identified. No association was observed between the study characteristics and spin presence, including the methodological quality of a systematic review. CONCLUSIONS: Overall, spin is infrequently observed in abstracts of systematic reviews covering diabetic neuropathy treatments. When comparing our results to other fields of medicine, the field of diabetic neuropathy research publishes systematic reviews whose abstracts mostly portray the findings of the review's full-text to reflect the results adequately.

Evaluation of spin in the abstracts of systematic reviews and meta-analyses relating to postoperative nausea and vomiting.

INTRODUCTION: Spin - the beautification of study results to emphasise benefits or minimise harms - is a deceptive reporting strategy with the potential to affect clinical decision-making adversely. Few studies have investigated the extent of spin in systematic reviews. Here, we sought to address this gap by evaluating the presence of the nine most severe forms of spin in the abstracts of systematic reviews on treatments for postoperative nausea and vomiting (PONV). PONV has the potential to increase hospital costs and patient burden, adversely affecting outcomes. METHODS: We developed search strategies for MEDLINE and Embase to identify systematic reviews focused on PONV. Following title and abstract screening of the reviews identified during the initial search, those that met inclusion criteria were evaluated for the presence of spin and received a revised AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews) appraisal by two investigators in a masked, duplicate manner. Study characteristics for each review were also extracted in duplicate. RESULTS: Our systematic search returned 3513 studies, of which 130 systematic reviews and meta-analyses were eligible for data extraction. We found that 29.2% of included systematic reviews contained spin (38/130). Eight of the nine types of spin were identified, with spin type 3 ('selective reporting of or overemphasis on efficacy outcomes or analysis favouring the beneficial effect of the experimental intervention') being the most common. Associations were found between spin and funding source. Spin was more likely in the abstracts of privately funded than nonfunded studies, odds ratio (OR) 2.81 [95% confidence interval (CI), 0.66 to 11.98]. In the abstracts of studies not mentioning funding spin was also more likely than in nonfunded studies, OR 2.30 (95% CI, 0.61 to 8.70). Neither of these results were statistically significant. Significance was found in the association between the presence of spin and AMSTAR-2 ratings: 'low' quality studies were less likely to contain spin than 'high' quality, OR 0.24 (95% CI, 0.07 to 0.88): 'critically low' studies were also less likely to contain spin than 'high' quality studies, OR 0.21 (95% CI, 0.07 to 0.65). There were no other associations between spin and the remaining extracted study characteristics or AMSTAR-2 ratings. CONCLUSION: Spin was present in greater than 29% of abstracts of systematic reviews and meta-analyses regarding PONV. Various stakeholders must take steps to improve the reporting quality of abstracts on PONV.

A brief analysis of challenges in implementing telehealth in a rural setting.

Available literature clearly indicates that successful implementation of telemedicine and telehealth has been a challenge. This challenge is further amplified if the reader must consider this implementation in a rural setting. In this article the authors discuss some of the key challenges associated with this implementation. The article sheds light on a few key studies and commentaries associated with the use of telehealth in a rural setting. Critically, the article summarizes these critical findings; thereby, informing the reader on the bottlenecks associated with the use of telehealth in a geographically rural area. Also, briefly summarizing the existing body of knowledge on this topic of study. Furthermore, a case study briefly narrating the use of telemedicine and telehealth for rural Oklahoma is presented to advance our understanding of the situation in this field. Some of the critical details associated with this case study provides insights on some of the key challenges associated with the implementation of telehealth in a rural setting. This case study also provides insights on key workflow processes that helped the implementation of telehealth. Finally, the authors summarize the key challenges in the implementation of telehealth based on their perspective. Here it is important to inform the readers that this article is not a scientific review on the topic instead presents an opinion backed by facts and existing literature. Overall, the authors present a key discussion that can lead to advances in research and required innovations that might help in providing easy access to healthcare through telehealth.

Towards the Development of a Substance Abuse Index (SEI) through Informatics.

Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g., pharmaceutical advertising campaigns). However, currently there is no comprehensive computational or machine learning framework that allows systematic studies of substance abuse and its factors with majority of the works using subjective surveys questionnaires and focusing on classification techniques. Lacking standardized methods and/or measures to prescribe medication and to study substance abuse makes it difficult to advance through collective research efforts. Thus, in this paper, we propose to test the feasibility of developing a, objective substance effect index, SEI, that can measure the tendency of an individual towards substance abuse. To that end, we combine the benefits of Electronics Medical Records (EMR) with machine learning technology by defining SEI as a function of EMR data and using logistics regression to obtain a closed form expression for SEI. We conduct various evaluations to validate the proposed model, and the results show that further work towards the development of SEI will not only provide researchers with standard computational measure for substance abuse, but may also allow them to study certain attribute interactions to gain further insights into substance abuse tendencies.

Spin Infrequently Occurs in Abstracts of Systematic Reviews For The Pharmacological Treatment of Type 2 Diabetes Mellitus.

AIMS: Currently, there is a growing body of research demonstrating that spin - the misinterpretation and distortion of a study's findings - is common in different fields of medicine. To our knowledge, no study has investigated its presence in systematic reviews focused on diabetic therapies. METHODS: We performed a cross-sectional study by searching MEDLINE and Embase for systematic reviews focused on pharmacologic treatments for type 2 diabetes mellitus. Our search retrieved 26,490 records, from which 199 studies were extracted in a masked, duplicate fashion. Each study was evaluated for the nine most severe types of spin and other study design parameters. Spin was presented as frequencies and odds ratios to identify associations between study characteristics. RESULTS: Spin was identified in the abstracts of 15 systematic reviews (15/199, 7.5%). Spin type 5 was the most common type identified (7/199, 3.5%). Spin types 1, 2, 4, and 8 were not identified. In the last 5 years (2016-2021), 7 systematic reviews contained spin within their abstract. There was no association between spins presence and any extracted study characteristic . CONCLUSIONS: Our findings show that spin infrequently occurs in abstracts of systematic reviews focused on pharmacologic therapies for type 2 diabetes mellitus. However, any amount of spin can lead to the distortion of a reader's interpretation of the study's findings. Thus, we provide recommendations with rationale to prevent spin in future systematic reviews.

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records.

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

Evaluation of spin in the abstracts of systematic reviews and meta-analyses on breast cancer treatment, screening, and quality of life outcomes: A cross-sectional study.

BACKGROUND: While spin - i.e., a reporting practice that embellishes positive findings and understates negative ones - is prevalent in randomized controlled trials, it has yet to be investigated in the context of systematic reviews. Owing to their significant role in clinical decision making and patient outcomes, this study seeks to identify and evaluate the severity of spin in the abstracts of systematic reviews on breast cancer. METHODS: We searched MEDLINE and Embase for systematic reviews and meta-analyses focused on breast cancer treatment, screening, and post-treatment quality of life between 1987 and 2020. Investigators independently screened for study selection, extracted spin data, and appraised the methodological quality of reviews using AMSTAR 2. In this cross-sectional study, 11,717 articles were identified, of which 581 met inclusion criteria. Following randomization, the first 200 were evaluated and 21 % contained evidence of at least one of nine types of spin. RESULTS: We identified spin types one, three, four, five, and six but not two, seven, eight, or nine. In particular, pharmacological (AOR 4.36, 95 % CI [1.18-16.01]) and surgical (AOR, 10.10 95 % CI [1.60-63.68]) intervention-type studies were highly associated with spin. There were no other associations between study characteristics and spin. While these results are significant, they contain a wide confidence interval and the reader should draw conclusions accordingly. CONCLUSIONS: There is evidence of spin in meta-analyses and systematic reviews regarding breast cancer treatment and quality of life outcomes. Accordingly, readers of systematic review abstracts related to breast cancer could be misled by distorted presentation of findings. POLICY SUMMARY: This study aims to improve the standards of reporting in systematic reviews and meta-analyses related to cancer.

Evaluation of Spin in the Abstracts of Systematic Reviews and Meta-analyses Covering Treatments for Achilles Tendon Ruptures.

BACKGROUND: Approximately 18 in every 100 000 people have experienced a ruptured Achilles tendon. Despite the prevalence of this condition, treatment options remain contested. HYPOTHESIS/PURPOSE: The objective of this study was to evaluate the use of spin-reporting practices that may exaggerate benefit or minimize harm-in abstracts of systematic reviews related to Achilles tendon repair. We also evaluated whether particular study characteristics were associated with spin. STUDY DESIGN: Cross-sectional. METHODS: We developed a search strategy for Ovid MEDLINE and Ovid Embase for systematic reviews focused on Achilles tendon treatment. Following title and abstract screening of these search returns, these reviews were evaluated for spin (according to a previously developed classification scheme) and received AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews-2) appraisals by 2 investigators in a masked, duplicate manner. Study characteristics for each review were also extracted in duplicate. RESULTS: Our systematic search returned 251 articles of which 43 systematic reviews and meta-analyses were eligible for data extraction. We found that 65.1% of included studies contained spin (28/43). Spin type 3 was the most common type, occurring in 53.5% (23/43) of abstracts. Spin types 5, 6, 1, and 4 occurred in 16.3% (7/43), 9.3% (4/43), 7% (3/43), and 5.3% (1/43), respectively. Spin types 2, 7, 8, and 9 did not occur. AMSTAR-2 appraised 32.6% (14/43) of the studies as "moderate" quality, 32.6% (14/43) as "low" quality, and 34.9% (15/43) as "critically low" quality. No systematic reviews were rated as "high" quality. There was no significant association between the presence of spin and the following study characteristics: intervention type, article discussing Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) adherence, journal recommending PRISMA adherence, funding sources, journal 5-year impact factor, year the review was received for publication, or AMSTAR-2 critical appraisals. CONCLUSION: Spin was present in abstracts of systematic reviews and meta-analyses-covering Achilles tendon tear treatment. Steps should be taken to improve the reporting quality of abstracts on Achilles tendon treatment as well as other common orthopedic conditions. CLINICAL RELEVANCE: In order to avoid negative patient outcomes, articles should be free of spin within the abstract.

Readmission Rates After Hip Fracture: Are There Prefracture Warning Signs for Patients Most at Risk of Readmission?

BACKGROUND: The purpose of this study was to evaluate known and yet unknown risk factors associated with readmission to the hospital within 30 days after hip fracture. METHODS: In this study, we used the Cerner Health Facts Electronic Health Record database data from January to August 2015. The univariate association of each variable (discharge location, demographic details, and comorbidities) against the 30-day readmission status was evaluated using the Chi-square test or the Fisher exact test. The significant variables (P < 0.05) obtained by the univariate analysis were used to build the multivariate logistic regression model to evaluate the multivariate associations of the variables. RESULTS: Thirty-four thousand seven hundred ninety index admissions of 33,740 unique patients were included in the study cohort. The overall 30-day readmission rate for patients with hip fractures was 10.7%. We demonstrated a new variable not discussed in previous articles on this topic: patients with previous inpatient/emergency visits within the past year were more likely to be readmitted within 30 days after the hip fracture surgery (P < 0.001). CONCLUSION: For patients with hip fractures, particular efforts should be taken to optimize outcomes in those with recent hospitalizations and/or discharge to a location other than home.

Evaluation of spin in the abstracts of systematic reviews and meta-analyses covering surgical management, or quality of life after surgical management, of osteoarthritis of the knee.

Objective: Our primary objective was to identify the prevalence of spin - misleading reporting practices that overemphasize benefit or underemphasize harm - within the abstracts of systematic reviews and meta-analyses focused on surgical management of osteoarthritis of the knee. Methods: A search string was developed to search Ovid MEDLINE and Ovid Embase for articles pertaining to surgical management, or quality of life after surgical management, of osteoarthritis of the knee. Titles and abstracts were screened according to our protocol, developed a priori, followed by full-text evaluation for spin in included articles. Study characteristics were simultaneously extracted with spin data and each included study received an AMSTAR-2 quality appraisal. All procedures were performed by two examiners in a masked, duplicate fashion. Results: Of the 1419 articles returned, 96 systematic reviews qualified for inclusion. 35.4% of the included abstracts (34/96) contained at least one type of spin with a total of 36 occurrences (two abstracts contained two types of spin). Selective reporting favoring benefit (type 3; 15/36, 41.7%) was the most prevalent followed by selective reporting of harms (type 6; 7/36, 19.4%). None of the abstracts contained spin types 2, 4, or 8. We found no significant association between spin and either AMSTAR-2 rating or extracted study characteristics. Conclusion: Of the included systematic reviews and meta-analyses, 35.4% contained spin in their abstract. To improve the reliability of systematic reviews and meta-analyses, researchers should act to minimize spin in future abstracts.

SNP variable selection by generalized graph domination.

BACKGROUND: High-throughput sequencing technology has revolutionized both medical and biological research by generating exceedingly large numbers of genetic variants. The resulting datasets share a number of common characteristics that might lead to poor generalization capacity. Concerns include noise accumulated due to the large number of predictors, sparse information regarding the p≫n problem, and overfitting and model mis-identification resulting from spurious collinearity. Additionally, complex correlation patterns are present among variables. As a consequence, reliable variable selection techniques play a pivotal role in predictive analysis, generalization capability, and robustness in clustering, as well as interpretability of the derived models. METHODS AND FINDINGS: K-dominating set, a parameterized graph-theoretic generalization model, was used to model SNP (single nucleotide polymorphism) data as a similarity network and searched for representative SNP variables. In particular, each SNP was represented as a vertex in the graph, (dis)similarity measures such as correlation coefficients or pairwise linkage disequilibrium were estimated to describe the relationship between each pair of SNPs; a pair of vertices are adjacent, i.e. joined by an edge, if the pairwise similarity measure exceeds a user-specified threshold. A minimum k-dominating set in the SNP graph was then made as the smallest subset such that every SNP that is excluded from the subset has at least k neighbors in the selected ones. The strength of k-dominating set selection in identifying independent variables, and in culling representative variables that are highly correlated with others, was demonstrated by a simulated dataset. The advantages of k-dominating set variable selection were also illustrated in two applications: pedigree reconstruction using SNP profiles of 1,372 Douglas-fir trees, and species delineation for 226 grasshopper mouse samples. A C++ source code that implements SNP-SELECT and uses Gurobi optimization solver for the k-dominating set variable selection is available (https://github.com/transgenomicsosu/SNP-SELECT).